Historically, in order to understand or appreciate a particular topic, one would need to read a myriad of resources and manually synthesize the contents of the resources. Conclusions or theories or broadly-categorized “results” could then be made based on this synthesis. This, of course, is a time-intensive and user-specific process.
However, as digital information becomes more and more prevalent and an increasing number of resources become available in a digital format in online databases, there is an opportunity to automate the reading and understanding of resources in order to derive useful knowledge across a wide variety of topics and for any generic user.
In one example described in “Semantic Content Management for Enterprises and the Web,” by Sheth et al., a semantic search utilizing a hierarchical arrangement of categories for annotating or tagging content is disclosed. Two components for semantic analysis are disclosed—a definitional component and an assertional component. Metadata can also be utilized in classification. This approach therefore uses tagging techniques by looking at specific words contained within the text (keywords) and tags the text based on the availability and frequency of these keywords. Such keyword tagging is often not an accurate depiction of the text.
In another example, described in U.S. Patent Application Pub. No. 2014/0032574 to Kahn, entitled, “Natural Language Understanding Using Brain-Like Approach: Semantic Engine Using Brain-Like Approach (SEBLA) Derives Semantics of Words and Sentences,” a semantic engine utilizing Natural Language Understanding (NLU) to solve semantics and its sub-problems is disclosed. Each word and representations of the respective words and rules to combine them are implemented. A knowledge framework can be learned or refined for a specific domain using respective text corpora. This approach does not clarify the overall meaning and concepts in a given text. Rather, it detects the domain of the input text, but it does not get to the specifics of the meaning and understanding of the sentence to the word level.
In another example, described in U.S. Patent Application Pub. No. 2013/0138665 to Hu et al., entitled, “Methods of Evaluating Semantic Differences, Methods of Identifying Related Sets of Items in Semantic Spaces, and Systems and Computer Program Products for Implementing the Same,” a method of evaluating semantic differences between a first item in a first semantic space and a second item in a second semantic space using a nearest neighbors analysis is disclosed. This approach therefore counts words and compares word vectors. As a result, is flawed in that if three or more words have similar meanings but they are different in spelling, the Hu approach will not recognize the relation of these words.
Therefore, there is a need for systems and methods that provide real-time, accurate, and verifiable semantic analysis of digital information for varying resource sizes. There is further a need to distill the resulting analyses into results or subsequent actions based on the analysis.